Odor-induced emotion recognition based on average frequency band division of EEG signals

Hui-Rang Hou; Xiao-Nei Zhang; Qing-Hao Meng

doi:10.1016/j.jneumeth.2020.108599

Odor-induced emotion recognition based on average frequency band division of EEG signals

J Neurosci Methods. 2020 Jan 21:334:108599. doi: 10.1016/j.jneumeth.2020.108599. Online ahead of print.

Authors

Hui-Rang Hou¹, Xiao-Nei Zhang¹, Qing-Hao Meng²

Affiliations

¹ Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
² Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China. Electronic address: qh_meng@tju.edu.cn.

PMID: 31978490
DOI: 10.1016/j.jneumeth.2020.108599

Abstract

Background: Emotion recognition plays a key role in multimedia. To enhance the sensation of reality, smell has been incorporated into multimedia systems because it can directly stimulate memories and trigger strong emotions.

New method: For the recognition of olfactory-induced emotions, this study explored a combination method using a support vector machine (SVM) with an average frequency band division (AFBD) method, where the AFBD method was proposed to extract the power-spectral-density (PSD) features from electroencephalogram (EEG) signals induced by smelling different odors. The so-called AFBD method means that each PSD feature was calculated based on equal frequency bandwidths, rather than the traditional EEG rhythm-based bandwidth. Thirteen odors were used to induce olfactory EEGs and their corresponding emotions. These emotions were then divided into two types of emotions, pleasure and disgust, or five types of emotions that were very unpleasant, slightly unpleasant, neutral, slightly pleasant, and very pleasant.

Results: Comparison between the proposed SVM plus AFBD method and other methods found average accuracies of 98.9 % and 88.5 % for two- and five-emotion recognition, respectively. These values were considerably higher than those of other combination methods, such as the combinations of AFBD or EEG rhythm-based features with naive Bayesian, k-nearest neighbor classification, voting-extreme learning machine, and backpropagation neural network methods.

Conclusions: The SVM plus AFBD method represents a useful contribution to olfactory-induced emotion recognition. Classification of the five-emotion categories was generally inferior to the classification of the two-emotion categories, suggesting that the recognition performance decreased as the number of emotions in the category increased.

Keywords: Average frequency band division; Emotion recognition; Machine learning; Olfactory EEG.